Space Pruning

Space pruning, a technique for reducing the computational cost and memory footprint of large models, focuses on efficiently removing less important parts of a model or dataset without significantly sacrificing performance. Current research explores various pruning strategies, including heuristic-based methods (like removing long files in code generation or low-performing candidates in virtual screening), learned pruning approaches (e.g., using neural tangent kernels in federated learning or co-evolutionary algorithms), and methods that integrate pruning with neural architecture search. These advancements are significant because they enable the training and deployment of complex models on resource-constrained devices and improve the efficiency of computationally expensive tasks like hyperparameter optimization and large-scale virtual screening.